# Exploring the Quantum Realm: The Variational Quantum Eigensolver (VQE) on Classiq

In the fascinating world of quantum computing, Richard Feynman's vision stands as a cornerstone. He posited that the quantum nature of the universe could best be explored and simulated through quantum computing rather than classical computing. This insight has led to the development of algorithms like the Variational Quantum Eigensolver (VQE), which is revolutionizing our approach to complex problems in physics and chemistry.

The VQE algorithm offers an opportunity to delve into the electronic structures of molecules and the design of new materials. This has major implications in fields ranging from pharmaceuticals to new energy sources.

Moreover, VQE is an algorithm more resilient to noises than most quantum algorithms because it uses shallow circuits, and hence, it is considered one of the first practical quantum algorithms that will give a valuable quantum advantage.

To obtain meaningful results with just a shallow circuit, it has parameters inside that are optimized with a classical computer. The measurement of the circuit is done according to the quantum property the chemist/physicist/material scientist needs to obtain, which usually cannot be achieved classically.

The Classiq platform takes this a step further with its powerful synthesis engine and high-level functional thinking. In contrast to other quantum programming approaches, where a developer painstakingly creates quantum circuits by deciding where each gate would be — a procedure that is resource-intensive and prone to human error.

The Classiq synthesis engine automates this process, improving efficiency and bringing better results by considering plenty of different implementations and deciding on the best implementation according to the developer's needs, such as the shallowest implementation, the shallowest implementation for specific hardware (see hardware-aware synthesis), and more. A common method in VQE circuits is using a UCC (Unitary Coupled Cluster) ansatz, which is defined via the exponentiation function. The latter refers to exponentiating a Hamiltonian, a function that uses many CNOT gates, many of which can cancel each other out and give a shallower circuit. For example, the Classiq platform showed an improvement of 53% in circuit depth and 48% fewer CNOT gates when synthesizing a UCC ansatz for the water molecule. This is not just a step but a leap forward in quantum computing.

By leveraging VQE on the Classiq platform, we're not just conducting research; we're opening doors to a new era of material science and molecular understanding. This combination of advanced algorithms, high-level modeling just like classical computing, and a sophisticated synthesis engine marks a significant milestone in our quantum journey, staying true to Feynman's vision.

In the fascinating world of quantum computing, Richard Feynman's vision stands as a cornerstone. He posited that the quantum nature of the universe could best be explored and simulated through quantum computing rather than classical computing. This insight has led to the development of algorithms like the Variational Quantum Eigensolver (VQE), which is revolutionizing our approach to complex problems in physics and chemistry.

The VQE algorithm offers an opportunity to delve into the electronic structures of molecules and the design of new materials. This has major implications in fields ranging from pharmaceuticals to new energy sources.

Moreover, VQE is an algorithm more resilient to noises than most quantum algorithms because it uses shallow circuits, and hence, it is considered one of the first practical quantum algorithms that will give a valuable quantum advantage.

To obtain meaningful results with just a shallow circuit, it has parameters inside that are optimized with a classical computer. The measurement of the circuit is done according to the quantum property the chemist/physicist/material scientist needs to obtain, which usually cannot be achieved classically.

The Classiq platform takes this a step further with its powerful synthesis engine and high-level functional thinking. In contrast to other quantum programming approaches, where a developer painstakingly creates quantum circuits by deciding where each gate would be — a procedure that is resource-intensive and prone to human error.

The Classiq synthesis engine automates this process, improving efficiency and bringing better results by considering plenty of different implementations and deciding on the best implementation according to the developer's needs, such as the shallowest implementation, the shallowest implementation for specific hardware (see hardware-aware synthesis), and more. A common method in VQE circuits is using a UCC (Unitary Coupled Cluster) ansatz, which is defined via the exponentiation function. The latter refers to exponentiating a Hamiltonian, a function that uses many CNOT gates, many of which can cancel each other out and give a shallower circuit. For example, the Classiq platform showed an improvement of 53% in circuit depth and 48% fewer CNOT gates when synthesizing a UCC ansatz for the water molecule. This is not just a step but a leap forward in quantum computing.

By leveraging VQE on the Classiq platform, we're not just conducting research; we're opening doors to a new era of material science and molecular understanding. This combination of advanced algorithms, high-level modeling just like classical computing, and a sophisticated synthesis engine marks a significant milestone in our quantum journey, staying true to Feynman's vision.

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